\section{Introduction}

Weak-supervised relation extraction is very promising because it
avoids tedious sentence laboring. But its performance is largely
limited by the number of seed instance it has.

Our goal is to learn robust extractor over the relation with few
instances.

We notice that there are many entity pairs in the large ontology
that are potential instances for the target relation. By matching
the target relation into the large ontology, we can identify these
instances to create the much stronger training data for the learner.

The challenge is (1) we need to join relations in the large ontology
to increase the recall, but it will bring some noises; (2) we should
consider the entity, entity pairs (instance level), concept class
and relations (ontology level) between the target ontology and the
large ontology altogether. There is no existing system doing this.

Briefly introduce our system, a concrete example with figure to show
the process of the data flow.

\subsection{Contributions}
\begin{enumerate}
  \item relation extraction with few instances
  \item join relations in the ontology to create huge number of new relations;
  \item an ontology matching algorithm jointly maps entity, entity pairs, concept class and relation into the large ontology;
  \item experiments shows our ontological smoothing relation extractor is effective and computational tractable as well.
\end{enumerate}
